{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Learn OpenAI Whisper - Chapter 5\n",
        "## Notebook 4: Voice and Image assistant with Whisper and LlaVa\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1NnvYEMQbAdaY5DtqXciytxL1d5UjnMIc)\n",
        "\n",
        "[LlaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf) is a multi-modal image-text to text model that can be seen as an \"open source version of GPT4-vision\".\n",
        "\n",
        "![image/png](https://cdn-uploads.huggingface.co/production/uploads/62441d1d9fdefb55a0b7d12c/FPshq08TKYD0e-qwPLDVO.png)\n",
        "\n",
        "The architecutre is a pure decoder-based text model that takes concatenated vision hidden states with text hidden states.\n",
        "\n",
        "We will leverage QLoRA quantization method and use `pipeline` to run our model.\n",
        "\n",
        "This notebook is designed to demonstrate the integration of OpenAI's Whisper and Hugging Face's LlaVa models to create a voice and image assistant. The notebook serves as a practical example of how to use these models in tandem to process and interpret both audio and visual data. The primary goal is to showcase the capabilities of LlaVa as a multi-modal image-text to text model, which is described as an \"open source version of GPT-4,\" and to illustrate how it can be combined with Whisper's audio processing to build a comprehensive multi-modal AI system.\n",
        "\n",
        "### Main code sections and their functions\n",
        "\n",
        "1. **Setting up the environment**: The initial code cells are responsible for installing the necessary libraries and dependencies, such as `transformers`, `bitsandbytes`, `accelerate`, `whisper`, `gradio`, and `gTTS`. Additionally, a temporary audio file is created using `ffmpeg` to facilitate audio processing.\n",
        "\n",
        "2. **Configuring quantization**: This section includes code to prepare the quantization configuration, which is essential for loading the LlaVa model with 4-bit precision. This step is crucial for optimizing the model's performance in terms of memory and speed.\n",
        "\n",
        "3. **Initializing the LlaVa model**: Here we log into the Hugging Face Hub and initialize the `image-to-text` pipeline with the LlaVa model, applying the quantization configuration prepared earlier. This pipeline is used to process images and generate descriptive text.\n",
        "\n",
        "4. **Processing images**: This section downloads a set of images and selects one to be processed. The selected image is then loaded and displayed using the `PIL` library.\n",
        "\n",
        "5. **Generating text from images**: This section prompts the LlaVa model to describe the loaded image in detail. It uses a specific format for the prompt and processes the output to extract and print the generated text.\n",
        "\n",
        "6. **Processiung audio**: The Whisper model is loaded, and a function is defined to transcribe audio input into text. This section also includes code to check for GPU availability, which is preferred for running Whisper.\n",
        "\n",
        "7. **Logging notebook activity**: A logging function is included to record the history of inputs and outputs, which can be useful for debugging and tracking the system's performance.\n",
        "\n",
        "8. **Defining supporting functions**: The following functions convert image-to-text output using `LlaVa`, transcribe speech-to-text using `Whisper`, and speech-to-text usin `gTTS` which is then saved as an audio file.\n",
        "\n",
        "9. **Running the Gradio interface**: The final section of the notebook sets up a Gradio interface that allows users to interact with the system by uploading images and providing voice input. The interface processes the inputs using the defined functions for image description and audio transcription, and it provides audio and text outputs.\n",
        "\n",
        "In summary, the notebook is structured to first set up the environment, then load and configure the models, followed by defining functions for processing images and audio, and finally, creating an interactive interface for users to engage with the multi-modal AI system."
      ],
      "metadata": {
        "id": "heEwc80MMkdv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **Setting up the environment**\n",
        "\n",
        "    The initial code cells are responsible for installing the necessary libraries and dependencies, such as `transformers`, `bitsandbytes`, `accelerate`, `whisper`, `gradio`, and `gTTS`. Additionally, a temporary audio file is created using `ffmpeg` to facilitate audio processing."
      ],
      "metadata": {
        "id": "zyamWgQgy-kc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H4UbrCWk0esW"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install -q -U transformers==4.37.2\n",
        "!pip install -q bitsandbytes==0.41.3 accelerate==0.25.0\n",
        "!pip install -q git+https://github.com/openai/whisper.git\n",
        "!pip install -q gradio\n",
        "!pip install -q gTTS\n",
        "!ffmpeg -f lavfi -i anullsrc=r=44100:cl=mono -t 10 -q:a 9 -acodec libmp3lame Temp.mp3"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing the quantization config to load the model in 4bit precision"
      ],
      "metadata": {
        "id": "Xvljx3nLMkdw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. **Configuring quantization**: This section includes code to prepare the quantization configuration, which is essential for loading the LlaVa model with 4-bit precision. This step is crucial for optimizing the model's performance in terms of memory and speed."
      ],
      "metadata": {
        "id": "f1LFpxIvzDYZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BitsAndBytesConfig\n",
        "\n",
        "quantization_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_compute_dtype=torch.float16\n",
        ")"
      ],
      "metadata": {
        "id": "GzUH8GUVMkdw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will leverage the `image-to-text` pipeline from transformers !\n",
        "\n",
        "3. **Initializing the LlaVa model**\n",
        "\n",
        "    Here we log into the Hugging Face Hub and initialize the `image-to-text` pipeline with the LlaVa model, applying the quantization configuration prepared earlier. This pipeline is used to process images and generate descriptive text."
      ],
      "metadata": {
        "id": "nKqh1XAjMkdw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b045a39e-2a3e-4153-bdb5-281500bcd348",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "e2d2b0ea4940460c944f822ed87b9199",
            "abf061f0e7ab425cbe158fb9b592f524"
          ]
        },
        "outputId": "edb54fcc-18d5-4c73-f7e6-c34f5c0edb6e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b19bcc41b54a4a019bc0145b7bfc894f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import whoami\n",
        "\n",
        "whoami()\n",
        "# you should see something like {'type': 'user',  'id': '...',  'name': 'Wauplin', ...}"
      ],
      "metadata": {
        "id": "t-Cj-HImlUSd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20f0708d-8416-4fa9-c657-fccdf6998780"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:80: UserWarning: \n",
            "Access to the secret `HF_TOKEN` has not been granted on this notebook.\n",
            "You will not be requested again.\n",
            "Please restart the session if you want to be prompted again.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'type': 'user',\n",
              " 'id': '6404c20d3d49e1e066b97c10',\n",
              " 'name': 'jbatista79',\n",
              " 'fullname': 'Josue Batista',\n",
              " 'email': 'josue@josuebatista.com',\n",
              " 'emailVerified': True,\n",
              " 'canPay': False,\n",
              " 'periodEnd': None,\n",
              " 'isPro': False,\n",
              " 'avatarUrl': '/avatars/e686904a94e267b4570907a7e734fbb4.svg',\n",
              " 'orgs': [],\n",
              " 'auth': {'type': 'access_token',\n",
              "  'accessToken': {'displayName': 'Whisper Fine-Tuning', 'role': 'write'}}}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "model_id = \"llava-hf/llava-1.5-7b-hf\"\n",
        "\n",
        "pipe = pipeline(\"image-to-text\", model=model_id, model_kwargs={\"quantization_config\": quantization_config})"
      ],
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        "colab": {
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        "outputId": "f9bb6291-fdad-4fe2-d3a6-9ec9456f8658",
        "id": "K_XzVqVpMkdx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/950 [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f64da6a8c08c440dbb7c796f5db8618b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00003.safetensors:   0%|          | 0.00/4.99G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a4cf6c9b8284c3e9f812f018c48f7c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00003.safetensors:   0%|          | 0.00/4.96G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd8056867f6e4b6eade8e22bf204bf58"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00003-of-00003.safetensors:   0%|          | 0.00/4.18G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e70afff07f342a6a32982578d783ebc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "603a926c21754c3ea322f5229a85d072"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/141 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f62bb7450044433999447564e1fe963"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.33k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6e60e5122804c4eabc36491582c6dee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2736e5919ecd4c919369b66be5f80bf8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4305bdafeb244743aadcf90d6f0d448e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/41.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "758ab6bafd844ecf8a04c416ac4c313d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/438 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8a5349750504041b999503e0d19670e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/557 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c48c31e83894b69b63e0b489fdadae0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to load the model in 4-bit precision, we need to pass a `quantization_config` to our model. Let's do that in the cells below"
      ],
      "metadata": {
        "id": "lvfB88HkMkdw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load an image"
      ],
      "metadata": {
        "id": "gAonn0uNMkdx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. **Processing images**\n",
        "\n",
        "    This section downloads a set of images and selects one to be processed. The selected image is then loaded and displayed using the `PIL` library."
      ],
      "metadata": {
        "id": "6POhkqiAzSZm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "59mp2cs106RA"
      },
      "outputs": [],
      "source": [
        "import whisper\n",
        "import gradio as gr\n",
        "import time\n",
        "import warnings\n",
        "import os\n",
        "from gtts import gTTS"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1, 11):\n",
        "    !wget -nv https://github.com/PacktPublishing/Learn-OpenAI-Whisper/raw/main/Chapter05/images/LOAIW_ch05_image_{str(i).zfill(2)}.jpg"
      ],
      "metadata": {
        "id": "SfBaccz5cp1H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fbd442b-e615-4890-ddf4-ec6d2bc5ee9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-04-11 13:28:14 URL:https://raw.githubusercontent.com/PacktPublishing/Learn-OpenAI-Whisper/main/Chapter05/images/LOAIW_ch05_image_01.jpg [24512/24512] -> \"LOAIW_ch05_image_01.jpg\" [1]\n",
            "2024-04-11 13:28:14 URL:https://raw.githubusercontent.com/PacktPublishing/Learn-OpenAI-Whisper/main/Chapter05/images/LOAIW_ch05_image_02.jpg [23737/23737] -> \"LOAIW_ch05_image_02.jpg\" [1]\n",
            "2024-04-11 13:28:15 URL:https://raw.githubusercontent.com/PacktPublishing/Learn-OpenAI-Whisper/main/Chapter05/images/LOAIW_ch05_image_03.jpg [40665/40665] -> \"LOAIW_ch05_image_03.jpg\" [1]\n",
            "2024-04-11 13:28:15 URL:https://raw.githubusercontent.com/PacktPublishing/Learn-OpenAI-Whisper/main/Chapter05/images/LOAIW_ch05_image_04.jpg [52700/52700] -> \"LOAIW_ch05_image_04.jpg\" [1]\n",
            "2024-04-11 13:28:15 URL:https://raw.githubusercontent.com/PacktPublishing/Learn-OpenAI-Whisper/main/Chapter05/images/LOAIW_ch05_image_05.jpg [48398/48398] -> \"LOAIW_ch05_image_05.jpg\" [1]\n",
            "2024-04-11 13:28:16 URL:https://raw.githubusercontent.com/PacktPublishing/Learn-OpenAI-Whisper/main/Chapter05/images/LOAIW_ch05_image_06.jpg [48599/48599] -> \"LOAIW_ch05_image_06.jpg\" [1]\n",
            "2024-04-11 13:28:16 URL:https://raw.githubusercontent.com/PacktPublishing/Learn-OpenAI-Whisper/main/Chapter05/images/LOAIW_ch05_image_07.jpg [48324/48324] -> \"LOAIW_ch05_image_07.jpg\" [1]\n",
            "2024-04-11 13:28:17 URL:https://raw.githubusercontent.com/PacktPublishing/Learn-OpenAI-Whisper/main/Chapter05/images/LOAIW_ch05_image_08.jpg [25989/25989] -> \"LOAIW_ch05_image_08.jpg\" [1]\n",
            "2024-04-11 13:28:17 URL:https://raw.githubusercontent.com/PacktPublishing/Learn-OpenAI-Whisper/main/Chapter05/images/LOAIW_ch05_image_09.jpg [42210/42210] -> \"LOAIW_ch05_image_09.jpg\" [1]\n",
            "2024-04-11 13:28:17 URL:https://raw.githubusercontent.com/PacktPublishing/Learn-OpenAI-Whisper/main/Chapter05/images/LOAIW_ch05_image_10.jpg [37021/37021] -> \"LOAIW_ch05_image_10.jpg\" [1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "\n",
        "image_path = \"/content/LOAIW_ch05_image_10.jpg\"\n",
        "image = Image.open((image_path))\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "546cdda5-591c-45a0-8abe-d53255795969",
        "id": "aqZfz0FWMkdx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=400x300>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the model using `pipeline`"
      ],
      "metadata": {
        "id": "dkDv9DJ-QjDm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. **Generating text from images**\n",
        "    This section prompts the LlaVa model to describe the loaded image in detail. It uses a specific format for the prompt and processes the output to extract and print the generated text.\n",
        "\n",
        "It is important to prompt the model wth a specific format, which is:\n",
        "```bash\n",
        "USER: <image>\\n<prompt>\\nASSISTANT:\n",
        "```"
      ],
      "metadata": {
        "id": "mqG1M4vyMkdx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk import sent_tokenize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDZPO6dPr8Ew",
        "outputId": "6cb9cb34-5900-4895-d973-b8a6a3b0004b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_new_tokens = 200\n",
        "\n",
        "prompt_instructions = \"\"\"\n",
        "Describe the image using as much detail as possible, is it a painting, a photograph, what colors are predominant, what is the image about?\n",
        "\"\"\"\n",
        "\n",
        "prompt = \"USER: <image>\\n\" + prompt_instructions + \"\\nASSISTANT:\"\n",
        "\n",
        "outputs = pipe(image, prompt=prompt, generate_kwargs={\"max_new_tokens\": 200})\n",
        "# outputs\n",
        "# print(outputs[0][\"generated_text\"])\n",
        "for sent in sent_tokenize(outputs[0][\"generated_text\"]):\n",
        "    print(sent)"
      ],
      "metadata": {
        "id": "W48r3NxDRskb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01e8b68c-90a9-49dc-9019-54c8d3c9d92b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "USER:  \n",
            "\n",
            "Describe the image using as much detail as possible, is it a painting, a photograph, what colors are predominant, what is the image about?\n",
            "ASSISTANT: The image is a photograph of a harbor with several boats docked.\n",
            "The boats are of various sizes and are lined up along the pier.\n",
            "The harbor is situated next to a large building, which could be a hotel or a commercial building.\n",
            "There are several people in the scene, some of them standing near the boats, while others are scattered throughout the harbor area.\n",
            "A few chairs are also present, with one chair located near the left side of the image and another chair closer to the right side.\n",
            "The overall atmosphere of the scene suggests a bustling harbor with people enjoying the view and the boats.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. **Processing speech-to-text**\n",
        "\n",
        "    The Whisper model is loaded, and a function is defined to transcribe audio input into text. This section also includes code to check for GPU availability, which is preferred for running Whisper."
      ],
      "metadata": {
        "id": "pblcrNgZfK-f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "from gtts import gTTS\n",
        "import numpy as np\n",
        "import torch\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "torch.cuda.is_available()\n",
        "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(f\"Using torch {torch.__version__} ({DEVICE})\")"
      ],
      "metadata": {
        "id": "K4c5uwE0e5q-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27e4bf78-9c1d-470b-aebd-341144fb7843"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using torch 2.2.1+cu121 (cuda)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Please keep in mind, that there are multiple different models available. You can find all of them [here](https://github.com/openai/whisper/blob/main/model-card.md). Each one of them has tradeoffs between accuracy and speed (compute needed). We will use the 'medium' model for this tutorial.\n",
        "\n",
        "Using a GPU is the preferred way to use Whisper. If you are using a local machine, you can check if you have a GPU available. The first line results `False`, if Cuda compatible Nvidia GPU is not available and `True` if it is available. The second line of code sets the model to preference GPU whenever it is available."
      ],
      "metadata": {
        "id": "mANIL6_Hf6X2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Now we can load the Whipser model. The model is loaded with the following command:\n",
        "import whisper\n",
        "model = whisper.load_model(\"medium\", device=DEVICE)\n",
        "print(\n",
        "    f\"Model is {'multilingual' if model.is_multilingual else 'English-only'} \"\n",
        "    f\"and has {sum(np.prod(p.shape) for p in model.parameters()):,} parameters.\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZm7wQ6vfdRQ",
        "outputId": "4d1e777c-7989-4960-9161-3539eb84a7be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████████████████████████████████| 1.42G/1.42G [00:12<00:00, 122MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model is multilingual and has 762,321,920 parameters.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import re\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "input_text = 'Describe the largest boat in the image.'\n",
        "input_image = '/content/LOAIW_ch05_image_10.jpg'\n",
        "\n",
        "# load the image\n",
        "image = Image.open(input_image)\n",
        "\n",
        "# print(input_text)\n",
        "prompt_instructions = \"\"\"\n",
        "Act as an expert in imagery descriptive analysis, using as much detail as possible from the image, respond to the following prompt:\n",
        "\"\"\" + input_text\n",
        "\n",
        "prompt = \"USER: <image>\\n\" + prompt_instructions + \"\\nASSISTANT:\"\n",
        "outputs = pipe(image, prompt=prompt, generate_kwargs={\"max_new_tokens\": 200})\n",
        "\n",
        "match = re.search(r'ASSISTANT:\\s*(.*)', outputs[0][\"generated_text\"])\n",
        "\n",
        "if match:\n",
        "    # Extract the text after \"ASSISTANT:\"\n",
        "    extracted_text = match.group(1)\n",
        "    print(extracted_text)\n",
        "else:\n",
        "    print(\"No match found.\")\n",
        "\n",
        "for sent in sent_tokenize(outputs[0][\"generated_text\"]):\n",
        "    print(sent)"
      ],
      "metadata": {
        "id": "wHv6fE6y1r09",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a872ced2-5eeb-4e2c-a3a5-5a4a49b83143"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The largest boat in the image is a white yacht, which is docked at a pier. It is a prominent feature in the scene, and its size and design make it stand out among the other boats in the harbor. The yacht appears to be well-maintained and ready for use, suggesting that it may be a popular choice among boat owners or enthusiasts.\n",
            "USER:  \n",
            "\n",
            "Act as an expert in imagery descriptive analysis, using as much detail as possible from the image, respond to the following prompt:\n",
            "Describe the largest boat in the image.\n",
            "ASSISTANT: The largest boat in the image is a white yacht, which is docked at a pier.\n",
            "It is a prominent feature in the scene, and its size and design make it stand out among the other boats in the harbor.\n",
            "The yacht appears to be well-maintained and ready for use, suggesting that it may be a popular choice among boat owners or enthusiasts.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "7. **Defining supporting functions**\n",
        "\n",
        "    The following cells define functions that convert image-to-text output using `LlaVa`, transcribe speech-to-text using `Whisper`, and speech-to-text using `gTTS` which is then saved as an audio file.\n"
      ],
      "metadata": {
        "id": "kqhgvj4H0W5O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import requests\n",
        "from PIL import Image\n",
        "\n",
        "def img2txt(input_text, input_image):\n",
        "\n",
        "    # load the image\n",
        "    image = Image.open(input_image)\n",
        "\n",
        "    # writehistory(f\"Input text: {input_text} - Type: {type(input_text)} - Dir: {dir(input_text)}\")\n",
        "    if type(input_text) == tuple:\n",
        "        prompt_instructions = \"\"\"\n",
        "        Describe the image using as much detail as possible, is it a painting, a photograph, what colors are predominant, what is the image about?\n",
        "        \"\"\"\n",
        "    else:\n",
        "        prompt_instructions = \"\"\"\n",
        "        Act as an expert in imagery descriptive analysis, using as much detail as possible from the image, respond to the following prompt:\n",
        "        \"\"\" + input_text\n",
        "\n",
        "    prompt = \"USER: <image>\\n\" + prompt_instructions + \"\\nASSISTANT:\"\n",
        "\n",
        "    outputs = pipe(image, prompt=prompt, generate_kwargs={\"max_new_tokens\": 200})\n",
        "\n",
        "    # Properly extract the response text\n",
        "    if outputs is not None and len(outputs[0][\"generated_text\"]) > 0:\n",
        "        match = re.search(r'ASSISTANT:\\s*(.*)', outputs[0][\"generated_text\"])\n",
        "        if match:\n",
        "            # Extract the text after \"ASSISTANT:\"\n",
        "            reply = match.group(1)\n",
        "        else:\n",
        "            reply = \"No response found.\"\n",
        "    else:\n",
        "        reply = \"No response generated.\"\n",
        "\n",
        "    return reply"
      ],
      "metadata": {
        "id": "DVo04xYV_HxE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def transcribe(audio):\n",
        "\n",
        "    # Check if the audio input is None or empty\n",
        "    if audio is None or audio == '':\n",
        "        return ('','',None)  # Return empty strings and None audio file\n",
        "\n",
        "    # language = 'en'\n",
        "\n",
        "    audio = whisper.load_audio(audio)\n",
        "    audio = whisper.pad_or_trim(audio)\n",
        "\n",
        "    mel = whisper.log_mel_spectrogram(audio).to(model.device)\n",
        "\n",
        "    _, probs = model.detect_language(mel)\n",
        "\n",
        "    options = whisper.DecodingOptions()\n",
        "    result = whisper.decode(model, mel, options)\n",
        "    result_text = result.text\n",
        "\n",
        "    return result_text"
      ],
      "metadata": {
        "id": "_muMSOTq7Sto"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def text_to_speech(text, file_path):\n",
        "    language = 'en'\n",
        "\n",
        "    audioobj = gTTS(text = text,\n",
        "                    lang = language,\n",
        "                    slow = False)\n",
        "\n",
        "    audioobj.save(file_path)\n",
        "\n",
        "    return file_path"
      ],
      "metadata": {
        "id": "zWy3yHj9QBJP"
      },
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    },
    {
      "cell_type": "markdown",
      "source": [
        "8. **Running the Gradio interface**\n",
        "\n",
        "    The final section of the notebook sets up a Gradio interface that allows users to interact with the system by uploading images and providing voice input. The interface processes the inputs using the defined functions for image description and audio transcription, and it provides audio and text outputs."
      ],
      "metadata": {
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      }
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      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "import base64\n",
        "import os\n",
        "\n",
        "# A function to handle audio and image inputs\n",
        "def process_inputs(audio_path, image_path):\n",
        "    # Process the audio file (assuming this is handled by a function called 'transcribe')\n",
        "    speech_to_text_output = transcribe(audio_path)\n",
        "\n",
        "    # Handle the image input\n",
        "    if image_path:\n",
        "        chatgpt_output = img2txt(speech_to_text_output, image_path)\n",
        "    else:\n",
        "        chatgpt_output = \"No image provided.\"\n",
        "\n",
        "    # Assuming 'transcribe' also returns the path to a processed audio file\n",
        "    processed_audio_path = text_to_speech(chatgpt_output, \"Temp3.mp3\")  # Replace with actual path if different\n",
        "\n",
        "    return speech_to_text_output, chatgpt_output, processed_audio_path\n",
        "\n",
        "# Create the interface\n",
        "iface = gr.Interface(\n",
        "    fn=process_inputs,\n",
        "    inputs=[\n",
        "        gr.Audio(sources=[\"microphone\"], type=\"filepath\"),\n",
        "        gr.Image(type=\"filepath\")\n",
        "    ],\n",
        "    outputs=[\n",
        "        gr.Textbox(label=\"Speech to Text\"),\n",
        "        gr.Textbox(label=\"ChatGPT Output\"),\n",
        "        gr.Audio(\"Temp.mp3\")\n",
        "    ],\n",
        "    title=\"Learn OpenAI Whisper: Image processing with Whisper, LlaVa, and gTTS\",\n",
        "    description=\"Upload an image and interact via voice input and audio response.\"\n",
        ")\n",
        "\n",
        "# Launch the interface\n",
        "iface.launch(debug=False)"
      ],
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            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://5bd79d5649e44dfd18.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
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